{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append(\"../..\")\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import ShuffleSplit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set the train sizes \n",
    "train_sizes = np.linspace(.1, 1.0, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yellowbrick.classifier.learning_curve import LearningCurveVisualizer "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing using GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FjqIoKIp/cpxyjwerquGwasTHWKU0LlpMVVXcbnfUt3mLyPL5fM36NyLhHWXq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115398cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = LearningCurveVisualizer(GaussianNB())\n",
    "viz.fit(X,y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing using GaussianNB with Varying Training Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ev1M6IwtyGFmQw9C8LGzaweGDDslJvLRoA5tKKumVncqfRvRpFve1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\nHwITq2ZHUaSNIZEcihRviUTSbjBMHZ/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KwaxRRTBHsG2pEAJ6yciZ5UREUcCb\nkDHIH9xA5CeD+R06vQ/fnPwUicZkXDtqCRKNSRGVJ+kkmI08bISIKBoY3jHGd6Rni6ZlHj7zFfaf\n+AhmgxWzRhXBYuoXUXlCJ2A28rARIqJo4bB5DFFVBQ5vK3RCu+VWR+vLUHrsA5j0ibh2VBGSzKkR\nlScgYNZbuSSMiCiKohbeqqri8ccfR0VFBYxGI4qLizFs2DAAQF1dHX7+858HnlteXo4VK1Zg8eLF\n0apOzFNUBS6PNieD+R0/+x2+qPoLjHICZo0qQnJCekTlqUJFgsES9g5sREQUnKiF90cffQS3240N\nGzZg//79WLt2LV566SUAQEZGBl599VUAwN/+9jc899xzWLhwYbSqEvMU1QunxsF9qrECe4+UQC8b\nMXNUIVISB0RUnu+wkQTIEWzkQkREwYlaeJeWlmLGjBkAgPHjx+PAgQOXPEcIgSeffBLPPvssZJm9\ntY4oqgdOj13T4K45dxi7D2+GLOkxI3cRUi2DIipPgIeNEBF1p6hNWGttbYXVemHSkizL8Hq97Z7z\nySefIDc3F9nZ2dGqRkxTFI/mPe7a5qP4vPId6CDhmtyFSE8aGlF5QgjIOj2XhBERdaOo9bytVits\nNlvge1VVode3f7v33nsPd999d9BldtR7j1WlpaVdPq6qChS4AA0nftnVBpxw7wUgMNQwGaePNeE0\nyiIrVAAGKbrBfbm2ovbYXsFjW4WG7RW8aLdV1MJ74sSJ2LZtG2677Tbs378feXl5lzznwIEDmDhx\nYtBljh07FiZT7A/NlpaWYtKkSZ0+7lFccHkcmpzF7Xe2tRrbKz4EdALTR96FwSm5EZcpdAKJhuSo\nziy/XFtRe2yv4LGtQsP2Cp5WbeVyuTrttEYtvG+66Sbs2rULhYWFEEJgzZo12LJlC+x2OxYtWoSG\nhgZYrVxSdDEtg/v42W9RXvM5mh31EAAAgYKcO7QJbi4JIyLqMVELb0mS8MQTT7S7lpOTE/g6NTUV\nJSUl0Xr7mOTxuuBSnJoF954jl7avEGrEZXNJGBFRz+IOa72E2+uAS3FA0mhyWnnN551c3x1RuVwS\nRkTU8xjevYDb64BbcUHS8K+j2VHf8XVnx9eDwSVhRES9A7dH7WFOjx1e1a1pcJ9tPXX+Hvelks3h\n7aImhIAscUkYEVFvwPDuQU63DYrq1XRW+ZnmY9j5/VtAJ/GdP6ggrHJ9p4RZIqgZERFpheHdQxzu\nVqiqouls7Zpzlfi8chMEBK4eeRdU1Yvymt1odtYj2ZyO/EEFyEq7IuRyhU4gwRDZEaFERKQdhnc3\nE0LAozo1D+4TDeXYe6QEOp2Ea0YuwA/6+XatCyes2+KSMCKi3ofh3Y2EEHC6WyGgahqGR+vL8EXV\nXyBLBszIW4iMpCxNylWFQIIxkUvCiIh6GYZ3N3J5HFCFtsFdeaYUXx37fzDKZszMK0SqdbAm5fqW\nhJkhS1wSRkTU2zC8u4mieHyzyjWcnHawZjfKTm6DSZ+IWaOKIj7W049LwoiIejeGdzcQQsCp2DUL\nbiEEvq3ege+qdyHRmIxZoxYjyZymWdlcEkZE1LsxvLuBy+sAVECLzdOEEPj6xMc4VLsPVlN/zBpV\nBIupX+QFn8clYUREvR/DO8oU1QOvos1wuSpUfHXsAxyp249kczpmjSpCgtF6+RcGiUvCiIhiA8M7\nioQQcHq1GS5XVQX7qv6M4w3fon/iDzAzrxAmQ6IGtfThkjAiotjB8I4it6LNcLmierH78LuoPncI\nadahmJG7EEa9WZM6AlwSRkQUaxjeUaKoHni9bugi7HV7FTd2Vb6D2uYqDEgejmtGzodeNmpUSy4J\nIyKKRQzvKHF6HREHt9vrxM7v30J960kMTslFQc4dkCXt/spUqDDKJi4JIyKKMQzvKHB7HYAqgAju\nH7u8duyoeBON9tPITM3H1BF/B0nDYW0hBPSSkUvCiIhiEMNbY4rqhdvrimiSmsPTih0V69HkqMOI\n9KswafhsTTd3AfxLwrSb8EZERN2H4a0xl9cRUdDaXE3YXvEGWl2NyB3wQ4zPuknzGeBcEkZEFNsY\n3hpyex0Qavh7l7c4G7C94g3Y3c3IH3Q1xg6ZpX1wc0kYEVHMY3hrRFEVuL1uSGGGYpP9DLYfWg+n\nx4Yrh1yL/MFXa1xDLgkjIuorGN4acXntYQd3g60GOyrehFtxYELWzcgd+EONa8clYUREfQnDWwOR\nDJfXtZzAzu/fgldxY/LwH2FExlWa149LwoiI+haGd4R8w+XhzS4/3VSFXZUboQoF03LmIjN1jOb1\n45IwIqK+h+EdIVeYe5efajyE3Yc3AwCmj7wLg1Nyta5a4HhPLgkjIupbGN4R8HhdYQ2XNyunUFG5\nH7Kkx/Tc+RiYPELzuvmC28DgJiLqgxjeYVJUBW4l+C1Qj5/9FuU1n6PJUQ9AQNbpMTNvMdKThmpe\nNxUq9LIRZg1PHSMiot6D4R0mt9cBHYIP7j1HStpdU4QXdncTAG3D2z85jfe4iYj6Lm333IwTHq8L\nqlCCfn55zeedXN+tVZUAMLiJiOIFe94hEkL1DZeH8Lmn2VHf8XVnx9fDoULAJCfAoOdyMCKivo49\n7xA5PfaQgtureDq9L55sTtekTqpQYZLNDG4iojjB8A6BR3FBEd6QXrP/xF87HWLPH1QQcZ2EUGEy\nsMdNRBRPGN5BEkKF2+uEFEKTHT/7LY7U7UdK4kBMGTEH/RIGQKeTYNIlYVr2XGSlXRFRnVShwmhI\n5M5pRERxhve8g+QbLg9+PXeLswFfHn0fesmIgpx5SDKnYXj6lQCAsm/KNAhuAbPBAr3MvcqJiOIN\ne95BCHW4XFG92H14M7yqG5OGz0aSOU3T+gioSDAkMriJiOIUe96XEc5w+dcnPsE5ey1GpF+FYRH2\nsC+pD3w9bp4ORkQUvxjel+H0OEIaLj/ZWIHKM18i2ZyOCVk3a1qXC8HNvzYionjGYfMueBQ3FOEJ\n+vk21zl8UfUXyJIeBSPv0HRY2xfcVgY3ERExvDsjhIDb6wh6uFxVFew+/C48ihMTsm5Bv4QM7eoS\nCG5ZszKJiCh2sRvXCVeIs8u/ObUdDbZqZKVegRHp4zSrh9AJJBqSgj4AhYiI+j4mQgc8ihveEIbL\na85VouL0HlhN/TFp+K0hHxHaGaEDg5uIiC7BnvdFQh0ut7tbsLdqCySdjIKcOzTdMMUX3Np8ECAi\nor6DXbqLhDJcrgoVe4+8C7fXgasyb0B/yw8ifn8hBHTQIcHI4CYioo4xvNvwKh54Q9iM5bvqnahr\nOYEh/Udh5IBJEb+/EAKSJMFstDK4iYioUxw2P08IAZfXDinIXndt81F8V70TFmM/TB7+o4jD1hfc\nMhKM1ojKISKivo/hfZ7L4wAEEEx2Oz2t2HukBDqdhGk582DUmyN6byEEZEkPs9ESUTlERBQfOGwO\nQFE88KruoHrPQgjsPbIFTo8N44ZehzTrkIje2xfcBgY3EREFLe7DWwgBp2KHFORyrIM1u1HbXIVB\n/UYib+CUiN5bhQpZNsBsTIyoHCIiii9xP2zu8joAFUENl9e1nMCBU9uRYEjClBG3R3SfW4UKo2yC\nUZ8QdhlERBSf4rrnrageeJXghstdXjv2HH4XADAtZy5MhvB7y0IwuImIKHxxHd5Ob3DD5UII1SYo\nGwAAEMpJREFU7DvyZzg8LbhiyExkJGWF/Z4qBGQYGdxERBS2uA1vVSgQqgjquYdqv0BNUyUGJg/H\n6EEF4b8nBEyymWdxExFRROI2vIN1trUa35z8BGa9BVOy/y7oiW0XExAwykYY9Nptn0pERPGJ4d0F\nt9eJPUfehSpUTM3+OyQYwttARQgBWafnUDkREWmC4d0JIQS+PLoVNtc55A+6GgP7jQi7LJ1Ox3Xc\nRESkmagtFVNVFY8//jgqKipgNBpRXFyMYcOGBR4vKyvD2rVrIYRARkYGnnnmGZhMvWdI+XDdVzjZ\neBDp1qG4YsjMsMsREEg0JmtYMyIiindR63l/9NFHcLvd2LBhA1asWIG1a9cGHhNCYNWqVXjqqaew\nfv16zJgxA6dOnYpWVULWaK/F/uMfwahPwLSceRHc51ZhNvCQESIi0lbUet6lpaWYMWMGAGD8+PE4\ncOBA4LGqqiqkpKTgT3/6E77//nvMmjUL2dnZ0apKSDyKG3sOb4YqFEwZMSfsXrMQKswGC2RJ1riG\nREQU76IW3q2trbBaL0zwkmUZXq8Xer0ejY2N+Nvf/oZf//rXyMrKwrJlyzB27FgUFHS9DKvtB4BI\nCaHCIxzQtelVCyFQ49mPFrUBqXI26k/YUX+iLKyyZRi7XBJWWloaVr3jEdsqNGyv4LGtQsP2Cl60\n2ypq4W21WmGz2QLfq6oKvd73dikpKRg2bBhycnIAADNmzMCBAwcuG95jx47V7L64KhTYXS3thsSr\n6stQUXUKqZbBuG70grB6zUII6PUGmPSd78BWWlqKSZMiP/87HrCtQsP2Ch7bKjRsr+Bp1VYul6vT\nTmvU7nlPnDgRO3bsAADs378feXl5gccyMzNhs9lw7NgxAMCXX36J3NzcaFUlKE2OOnx17P/BIJsw\nLWde2MEtSXKXwU1ERBSpqPW8b7rpJuzatQuFhYUQQmDNmjXYsmUL7HY7Fi1ahN/+9rdYsWIFhBCY\nMGECrr322mhV5bK8igd7Dr8LRfVgas6dsJpSwipHp9PBbOCSMCIiiq6ohbckSXjiiSfaXfMPkwNA\nQUEBNm7cGK23D8n+E39Fk6MOIwdMwtDU0WGV4VsSlsSZ5UREFHVxv0nL8bPf4kjdfqQkDsRVmTeE\nVYZvSZil3eQ3IiKiaInrtGlxNuDLo+9DLxlRkDMPshT6QIQqVBj1iWG9loiIKBxxlzhH6r7GNye2\n4Zz9DHQ6CarwYmr2XCSZ00IuS4UKo94Mg2yMQk2JiIg6FlfhfaTua+yoWB/4XggVABDOXWohBPSS\nEUa9WaPaERERBSeuhs2/ObGtw+vlNbtDLkuSJJiNXBJGRETdL67C+5z9TIfXm531IZUjdIA5zONB\niYiIIhVX4Z2SOKDD68nm9KDLEBBI0Fu4JIyIiHpMXIX3lZnXdXg9f1DX27L6qULAbEiExMNGiIio\nB8XVhLXsjKsA+O59n3OcQbI5DfmDrkZW2hWXfa0KAZPB3OVhI0RERN0hrsIb8AV4dsZVHR5M0hkB\nAaNshEHW5lAUIiKiSMTVsHk4hBCQdXoY9Qk9XRUiIiIADO/L0ul0MBt52AgREfUeDO8uCJ1AgjGp\np6tBRETUDsO7EwICZr2VS8KIiKjXYXh3QBUqzPpEyFwSRkREvRDD+yIqVJgMCZBlLgkjIqLeieHd\nhhACRtnEJWFERNSrMbzPE0JAkmQuCSMiol6P4X2eTqeD2cAlYURE1PsxvHH+sBEjZ5YTEVFsiPvw\nFlBhNligC2KbVCIiot4grhNLhQqT3gJZirst3omIKIbFcXjrYJYToeeSMCIiijFxG96SToLRYO7p\nahAREYUsbsObiIgoVjG8iYiIYgzDm4iIKMYwvImIiGIMw5uIiCjGMLyJiIhiDMObiIgoxjC8iYiI\nYgzDm4iIKMYwvImIiGIMw5uIiCjGxMRxWkIIAIDb7e7hmmjH5XL1dBViBtsqNGyv4LGtQsP2Cp4W\nbeXPPH8GtqUTHV3tZVpaWnDo0KGergYREVG3y8vLQ1JSUrtrMRHeqqrCZrPBYDBAp9P1dHWIiIii\nTggBj8cDi8UCSWp/lzsmwpuIiIgu4IQ1IiKiGMPwJiIiijEMbyIiohjD8CYiIooxDO8o8Hg8WLly\nJYqKijB//nx8/PHHOHbsGBYvXoyioiL85je/gaqqAIC33noLd955JxYuXIht27b1cM17ztmzZzFr\n1iwcPnyYbXUZv//977Fo0SLceeedePvtt9lenfB4PFixYgUKCwtRVFTEn60ufP3111i6dCkAhNRG\nTqcTDz74IIqKivBP//RPaGho6LE/Q3dp21bl5eUoKirC0qVL8Y//+I+or68H0E1tJUhzGzduFMXF\nxUIIIRobG8WsWbPEfffdJ/bs2SOEEGLVqlXiww8/FGfOnBG33367cLlcorm5OfB1vHG73eL+++8X\nN998s6isrGRbdWHPnj3ivvvuE4qiiNbWVvHCCy+wvTrx17/+VTz00ENCCCF27twpHnjgAbZVB155\n5RVx++23iwULFgghREht9L//+7/ihRdeEEII8ec//1k8+eSTPfbn6A4Xt9WSJUvEd999J4QQYv36\n9WLNmjXd1lbseUfBrbfeiocffhiAb52eLMv49ttvMWXKFADAzJkz8fnnn6OsrAwTJkyA0WhEUlIS\nsrKycPDgwZ6seo94+umnUVhYiAEDBgAA26oLO3fuRF5eHpYvX45ly5bh2muvZXt1YsSIEVAUBaqq\norW1FXq9nm3VgaysLKxbty7wfShtVFpaihkzZgSeu3v37h75M3SXi9vq3//935Gfnw8AUBQFJpOp\n29qK4R0FFosFVqsVra2teOihh/Av//IvEEIENpixWCxoaWlBa2tru11zLBYLWltbe6raPWLTpk1I\nTU0N/FADYFt1obGxEQcOHMDzzz+P1atX4xe/+AXbqxOJiYk4deoUZs+ejVWrVmHp0qVsqw7ccsst\n0Osv7JQdShu1ve5/bl92cVv5OxxfffUVXnvtNdxzzz3d1lYxsbd5LKqpqcHy5ctRVFSEOXPm4Jln\nngk8ZrPZkJycDKvVCpvN1u76xVvg9XXvvPMOdDoddu/ejfLycjz66KPt7gWxrdpLSUlBdnY2jEYj\nsrOzYTKZcPr06cDjbK8L/vSnP+Gaa67BihUrUFNTg7//+7+Hx+MJPM626ljbnbwu10Ztr/ufG2+2\nbt2Kl156Ca+88gpSU1O7ra3Y846C+vp63HvvvVi5ciXmz58PABgzZgz27t0LANixYwd++MMfYty4\ncSgtLYXL5UJLSwsOHz6MvLy8nqx6t3v99dfx2muv4dVXX0V+fj6efvppzJw5k23ViUmTJuGzzz6D\nEAK1tbVwOBwoKChge3UgOTk5EML9+vWD1+vl/4dBCKWNJk6ciO3btweeO2nSpJ6sercrKSkJ/P7K\nzMwEgG5rK26PGgXFxcV4//33kZ2dHbj2q1/9CsXFxfB4PMjOzkZxcTFkWcZbb72FDRs2QAiB++67\nD7fccksP1rxnLV26FI8//jgkScKqVavYVp34t3/7N+zduxdCCPzsZz/D0KFD2V4dsNls+OUvf4m6\nujp4PB7cfffdGDt2LNuqAydPnsTPf/5zvPXWW6iqqgq6jRwOBx599FHU1dXBYDDgd7/7HTIyMnr6\njxNV/rZav349CgoKMGjQoEAvevLkyXjooYe6pa0Y3kRERDGGw+ZEREQxhuFNREQUYxjeREREMYbh\nTUREFGMY3kRERDGGm7QQ9YDVq1fjq6++gsfjwfHjx5GTkwMAuPvuu3HXXXcFVcbzzz+PsWPH4oYb\nbuj0OXPnzkVJSUnE9f3ggw/wyiuvwOv1QgiBuXPn4ic/+UnE5XZm06ZN2LdvH9auXRu19yCKZVwq\nRtSDTp48ibvvvhuffPJJT1elU7W1tSgsLMSmTZvQv39/2Gw2LF26FMuXL+/yg0MkGN5EXWPPm6iX\nWbduHfbv34+amhosWbIEubm5eO655+B0OtHU1ISVK1di9uzZ+Nd//VdMmTIFU6ZMwQMPPIDc3FyU\nl5cjLS0Nzz//PFJSUjBq1ChUVFRg3bp1qK2txbFjx3Dq1CksWLAAP/3pT+HxePCb3/wGpaWlGDhw\nIHQ6He6//35MnTo1UJ/GxkZ4PB44nU4Avn2Z165dC5PJBAB4//338cc//hFOpxMulwvFxcWYPHky\nli5divz8fOzevRtOpxOPPfYYXn31VVRWVuKee+7BPffcg3Xr1uHo0aM4fvw4zp07h0WLFl3Soy8r\nK8NTTz0Fp9OJ/v37Y/Xq1cjMzMQf//hHbN68GZIkYdy4cXjiiSe67y+JqIcxvIl6Ibfbja1btwIA\nHnroIRQXFyMnJwe7d+/GmjVrMHv27HbPP3jwINasWYMxY8bgwQcfxJYtWwJnDvtVVFTg9ddfR0tL\nC2688UYsWbIEJSUlcDgc+OCDD1BdXY05c+ZcUpfRo0fjhhtuwI033oj8/HxMnToVc+bMwbBhw6Cq\nKt588028/PLLSE1NxcaNG/GHP/wBkydPDrx+y5YtePHFF1FcXIz33nsPDQ0NmDdvHu655x4AwKFD\nh/Dmm29CVVXceeedKCgoaNcOjz32GF5++WUMHjwYn332GVatWoX/+Z//we9//3t89tlnkGUZq1ev\nRm1tLQYOHKjVXwFRr8bwJuqFxo0bF/j6mWeewbZt2/DBBx/g66+/bnfogV9aWhrGjBkDAMjNzUVT\nU9Mlz5k6dSqMRiPS0tKQkpKClpYW7Nq1CwsXLoROp8OQIUPaBWdbq1evxv3334+dO3di586dWLhw\nIZ599lncfPPN+M///E988sknqKqqwr59+9odbDFz5kwAwODBg3HVVVchISEBQ4YMQXNzc+A5t99+\nOywWCwDg+uuvx549e9C/f38AwNGjR3HixAn89Kc/DTzff7znhAkTMH/+fNxwww1YsmQJg5viCmeb\nE/VCZrM58HVRURHKysowduxYLFu2rMPn+4ewAUCn06GjqSwdPUeWZaiq2mVdPv30U2zduhUDBw7E\nXXfdheeeew6PPfYYNm7cCJvNhrvuugsnT54MDJW3ZTAYAl+3PUqxLVmWA1+rqnrJ90OHDkVJSQlK\nSkqwadMmvPHGGwCA//qv/8Ljjz8OIQR+8pOfYN++fV3+OYj6EoY3US927tw5HD16FA8//DBmzZqF\nXbt2QVEUzcq/+uqrsXXr1sApZfv27Quc5exnNpvxu9/9DidPngTgO++5srIS+fn5OHr0KCRJwrJl\nyzBt2jTs2LEj5Pp99NFHcLvdaGpqwrZt23DNNdcEHsvOzkZTUxO+/PJLAL4jZH/xi1+goaEBs2fP\nRl5eHh5++GFMnz4dFRUVEbYGUezgsDlRL5aSkoIFCxbgRz/6EaxWK8aPHw+n0wm73a5J+QsXLsTB\ngwcxZ84cZGRkYPDgwe16/QAwbdo0PPDAA1i2bFngPOwZM2Zg+fLlkGUZ+fn5mD17NsxmMyZPnozq\n6uqQ6mAymVBUVITW1lbcd999GDlyJMrKygAARqMRzz//PH7729/C5XLBarXi6aefRmpqKgoLCzF/\n/nwkJCRg0KBBuOOOOzRpE6JYwKViRHHs008/hRAC1113HVpaWjBv3jy88847SElJ6Zb3X7duHQDg\nwQcf7Jb3I+or2PMmimM5OTl45JFH8B//8R8AfDPbuyu4iSh87HkTERHFGE5YIyIiijEMbyIiohjD\n8CYiIooxDG8iIqIYw/AmIiKKMQxvIiKiGPP/AXfKyRZErc0LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11672b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = LearningCurveVisualizer(GaussianNB(), train_sizes=np.linspace(.1, 1.0, 15))\n",
    "viz.fit(X,y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing using GaussianNB with Varying Training Samples and Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fq/A3UrCDFe753gBHi6TWLoQQQkL9pLlsGm/eeA4/6ZnJFzuPcGccgj1kWLX2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\nbP92Ci2XxwmXx8HBb2GkwlOEfx3cHfKABZofoe0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IKHIw1INECIF9x7+AgMDQXuNDPv2p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\n1yAyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a84bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = LearningCurveVisualizer(GaussianNB(), \n",
    "                              train_sizes=np.linspace(.1, 1.0, 15),\n",
    "                              cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0))\n",
    "viz.fit(X,y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing using SVC with Varying Training Samples and Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3B1fj4Nnt8GruSrrRo5ES3750SpktBRZTdNDqR6FRuvwpLlpBjdPCiC7GUKfL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\nIYQQQiOkqAshhBAa8T+STzOZoDL0QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac73fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = LearningCurveVisualizer(SVC(kernel='linear'), \n",
    "                              train_sizes=np.linspace(0.1, 1.0, 5),\n",
    "                              cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0))\n",
    "viz.fit(X,y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:yellowbrick]",
   "language": "python",
   "name": "conda-env-yellowbrick-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
